The Pediatric Rheumatology Informatics Core is a funded component of a P30 Rheumatic Disease Core Center and a major contributor to the past success of the P01program. This core provides biomedical informatics expertise and infrastructure to the Cincinnati Rheumatic Diseases Core Center Research Base at the Cincinnati Children's Research Foundation of Cincinnati Children's Hospital Medical Center, and the University of Cincinnati College of Medicine. Because of the continued need for extensive microarray data management and analysis support by the proposed P01program, we are proposing to continue to fund a supplement to the Informatics Core. The purpose of the supplement will be to provide adequate bioinformatics infrastructure, expertise and hands on assistance for specific data management and analysis needs of the 4 P01 projects. The core will provide tailored analysis support to individual projects and perform integrative analyses of all data generated throughout the project. The Core will employ standardized, state- of-the-art protocols for management and analysis of microarray data and develop innovative statistical learning approaches for constructing and validating predictive transcriptional signatures. Altogether, the Core will facilitate the use of optimal data analysis procedures by integrating current relevant methodological, biological and clinical insights. The core will also facilitate the meaningful access to data by the scientific community through the development of new JIA Genomics Data Portal (http://JIAGenomics.org), and provide biostatistical support to research projects as needed. The three aims of the core are: 1) To provide research projects with the comprehensive Bioinformatics support for management, analysis and interpretation of DNA microarray data; 2) To develop the JIA genomics data portal (http://JlAGenomics.org) as the community resource for meaningful access to genomics data produced by P01 projects, and to provide the genomics data context for meaningful interpretation and analysis of this data; and 3) To apply innovative statistical methods for integrated analysis of JIA genomics data to facilitate the use of mechanistic insights in constructing predictive transcriptional signatures.